The Application of Remote Sensing and Machine Learning to Improve Early Warning Systems for Harmful Algal Events in the Highland Lake Chains, TX
Topics: Water Resources and Hydrology
, Remote Sensing
, Environment
Keywords: Water Quality, Chlorophyll-a, Remote Sensing, Deep Learning
Session Type: Virtual Guided Poster Abstract
Day: Sunday
Session Start / End Time: 2/27/2022 09:40 AM (Eastern Time (US & Canada)) - 2/27/2022 11:00 AM (Eastern Time (US & Canada))
Room: Virtual 1
Authors:
Shuyu Y Chang, Penn State University
Kaitlynn Hietpas, NASA DEVELOP Program - SSAI
Mark Radwin, NASA DEVELOP Program - SSAI
Emma Waugh, NASA DEVELOP Program - SSAI
Addison Pletcher, NASA DEVELOP Program - SSAI
Ryan Hammock, NASA DEVELOP Program - SSAI
Erin Urquhart, NASA Goddard Space Flight Center
Kimberly Van Meter, Penn State University
,
,
Abstract
Beginning in 2019, harmful algal events have caused canine deaths in both Lady Bird Lake and Lake Travis located near Austin, Texas. These two reservoirs are part of the larger Highland Lakes chain, managed by the City of Austin Department of Watershed Protection (COA DWP) and the Lower Colorado River Authority (LCRA), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable environmental conditions for algal events in Central Texas, LCRA and COA DWP partnered with NASA DEVELOP to improve monitoring and early detection of algal events, utilizing satellite remote sensing and machine learning. Spatially and temporally varied chlorophyll-a concentrations, cyanobacteria detections, turbidity, and water surface temperature products are used as environmental proxies. Chlorophyll-a concentrations were estimated using a pre-trained Mixture Density Network, and cyanobacteria detection was accomplished using the Broad Wavelength Algae Index, which can differentiate algal blooms from algal proliferations. In situ data were used to validate remotely sensed measurements and quantify uncertainties. Preliminary results appear to fit sufficiently well to in situ observations, suggesting that remote sensing data are feasible to retrieve biogeochemical properties and/or inherent optical properties (IOPs) of water columns in these inland human-made lakes. Uncertainties were introduced from the sensitivity to atmospheric correction, inherent mismatches between satellite and sampling data, and relatively lower signal to noise ratio over water. The resulting products enable near real-time monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain, and will ultimately support water management, decision making, and risk communication.
The Application of Remote Sensing and Machine Learning to Improve Early Warning Systems for Harmful Algal Events in the Highland Lake Chains, TX
Category
Virtual Guided Poster Abstract
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